Tutorial.ipynb

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   "source": [
    "# Evalica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237cb1a9-eaeb-4c79-84bc-2100de1f5b4e",
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   "source": [
    "[![Open in Colab][colab_badge]][colab_link] [![Binder][binder_badge]][binder_link]\n",
    "\n",
    "[colab_badge]: https://colab.research.google.com/assets/colab-badge.svg\n",
    "[colab_link]: https://colab.research.google.com/github/dustalov/evalica/blob/master/Tutorial.ipynb\n",
    "[binder_badge]: https://mybinder.org/badge_logo.svg\n",
    "[binder_link]: https://mybinder.org/v2/gh/dustalov/evalica/HEAD?labpath=Tutorial.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "![Evalica](Evalica.svg)"
   ]
  },
  {
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   "source": [
    "import evalica\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from evalica import Winner\n",
    "from plotly.graph_objects import Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfed1bc3-7b95-4af6-b234-f94240706e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "evalica.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e647667c-adb8-4f16-9b7c-993cf2739c2b",
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   "source": [
    "df_food = pd.read_csv(\"food.csv\", dtype=str)\n",
    "\n",
    "df_food[\"winner\"] = df_food[\"winner\"].map({\n",
    "    \"left\": Winner.X,\n",
    "    \"right\": Winner.Y,\n",
    "    \"tie\": Winner.Draw,\n",
    "})\n",
    "\n",
    "df_food.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b96c16f2-b364-4974-aa41-085564d40681",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_food[\"left_id\"], df_food[\"right_id\"], index = evalica.indexing(df_food[\"left\"], df_food[\"right\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec6ea649-04a5-4abb-961e-63afa4dfbc77",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrices = evalica.matrices(df_food[\"left_id\"], df_food[\"right_id\"], df_food[\"winner\"], index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42da86e0-c2c6-42c4-9c5e-84d56587fdea",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(matrices.win_matrix, index=index, columns=index)  # win matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5dc51f6-bca7-4153-8265-18c019e8a639",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(matrices.tie_matrix, index=index, columns=index)  # tie matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a75c4442-934a-47f1-8e19-18b73e436fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_result = evalica.counting(df_food[\"left\"], df_food[\"right\"], df_food[\"winner\"])\n",
    "count_result.scores.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce104025-26a4-4a8e-b2dd-1e97ede5592f",
   "metadata": {},
   "outputs": [],
   "source": [
    "avr_result = evalica.average_win_rate(df_food[\"left\"], df_food[\"right\"], df_food[\"winner\"])\n",
    "avr_result.scores.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d561c08e-4a52-435e-a847-b6adf44ab543",
   "metadata": {},
   "outputs": [],
   "source": [
    "bt_result = evalica.bradley_terry(df_food[\"left\"], df_food[\"right\"], df_food[\"winner\"])\n",
    "bt_result.scores.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64bf8f2b-a08f-46b9-b4df-e5d58d8c8a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "newman_result = evalica.newman(df_food[\"left\"], df_food[\"right\"], df_food[\"winner\"])\n",
    "newman_result.scores.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35cdcc91-d182-4d1c-842e-251052defcd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "eigen_result = evalica.eigen(df_food[\"left\"], df_food[\"right\"], df_food[\"winner\"])\n",
    "eigen_result.scores.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39c5d898-944c-472d-abc2-553c54503adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "elo_result = evalica.elo(df_food[\"left\"], df_food[\"right\"], df_food[\"winner\"])\n",
    "elo_result.scores.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e1a1c5-b32d-4988-92b3-7549fbad3845",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bt_pairwise = evalica.pairwise_frame(bt_result.scores)\n",
    "\n",
    "df_bt_pairwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "def visualize(df_pairwise: pd.DataFrame) -> Figure:\n",
    "    fig = px.imshow(df_pairwise, color_continuous_scale=\"RdBu\", text_auto=\".2f\")\n",
    "\n",
    "    fig.update_layout(xaxis_title=\"Loser\", yaxis_title=\"Winner\", xaxis_side=\"top\")\n",
    "\n",
    "    fig.update_traces(hovertemplate=\"Winner: %{y}<br>Loser: %{x}<br>Fraction of Wins: %{z}\")\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5241affc-7b81-49f1-9dd0-f2b7f03d2337",
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize(df_bt_pairwise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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